Arabitol, mannitol and glucose as tracers of primary ... · Arabitol, mannitol and glucose as...

Arabitol, mannitol and glucose as tracers of primary biogenic

organic aerosol: influence of environmental factors on ambient

air concentrations and spatial distribution over France

Abdoulaye Samaké1, Jean-Luc Jaffrezo1, Olivier Favez2, Samuël Weber1, Véronique Jacob1,

Trishalee Canete1, Alexandre Albinet2, Aurélie Charron1,16, Véronique Riffault3, Esperanza

Perdrix3, Antoine Waked1, Benjamin Golly1, Dalia Salameh1*, Florie Chevrier1,4, Diogo Miguel

Oliveira2,3, Jean-Luc Besombes4, Jean M.F. Martins1, Nicolas Bonnaire5, Sébastien Conil6,

Géraldine Guillaud7, Boualem Mesbah8, Benoit Rocq9, Pierre-Yves Robic10, Agnès Hulin11,

Sébastien Le Meur12, Maxence Descheemaecker13, Eve Chretien14, Nicolas Marchand15, and

Gaëlle Uzu1.

1University Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France 2INERIS, Parc Technologique Alata, BP 2, F-60550 Verneuil-en-Halatte, France 3IMT Lille Douai, University Lille, SAGE – Département Sciences de l’Atmosphère et Génie de l’Environnement,

59000 Lille, France 4University Savoie Mont-Blanc, LCME, 73000 Chambéry, France 5LSCE, UMR CNRS-CEA-UVSQ, 91191 Gif-sur Yvette, France 6ANDRA DRD/GES Observatoire Pérenne de l’Environnement, F-55290 Bure, France 7Atmo Auvergne-Rhône-Alpes, 38400 Grenoble, France 8Air PACA, 03040, France 9Atmo Hauts de France, 59000, France 10Atmo Occitanie, 31330 Toulouse, France 11Atmo Nouvelle Aquitaine, 33000, France 12Atmo Normandie, 76000, France 13Lig’Air, 45590 Saint-Cyr-en-Val, France 14Atmo Grand Est, 16034 Strasbourg, France 15University Aix Marseille, LCE (UMR7376), Marseille, France 16IFSTTAR, F-69675 Bron, France *Now at: Airport pollution control authority (ACNUSA), 75007 Paris, France

Corresponding author(s): A Samaké ([email protected]) and JL Jaffrezo (Jean-

[email protected])

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Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-434Manuscript under review for journal Atmos. Chem. Phys.Discussion started: 20 May 2019c© Author(s) 2019. CC BY 4.0 License.

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Abstract. The primary sugar compounds (SC, defined as glucose, arabitol and mannitol) are widely recognized as 1 suitable molecular markers to characterize and apportion primary biogenic organic aerosol emission sources. This 2 work improves our understanding of the spatial behavior and distribution of these chemical species and evidences 3 their major effective environmental drivers. We conducted a large study focusing on the daily (24 h) PM10 SC 4 concentrations for 16 increasing space scale sites (local to nation-wide), over at least one complete year. These 5 sites are distributed in several French geographic areas of different environmental conditions. Our analyses, mainly 6 based on the examination of the short-term evolutions of SC concentrations, clearly show distance-dependent 7 correlations. SC concentration evolutions are highly synchronous at an urban city-scale and remain well correlated 8 throughout the same geographic regions, even if the sites are situated in different cities. However, sampling sites 9 located in two distinct geographic areas are poorly correlated. Such pattern indicates that the processes responsible 10 for the evolution of the atmospheric SC concentrations present a spatial homogeneity over typical areas of at least 11 tens of kilometers. Local phenomena, such as resuspension of topsoil and associated microbiota, do no account for 12 the major emissions processes of SC in urban areas not directly influenced by agricultural activities. The 13 concentrations of SC and cellulose display remarkably synchronous temporal evolution cycles at an urban site in 14 Grenoble, indicating a common source ascribed to vegetation. Additionally, higher concentrations of SC at another 15 site located in a crop field region occur during each harvest periods, pointing out resuspension processes of plant 16 materials (crop detritus, leaf debris) and associated microbiota for agricultural and nearby urbanized areas. Finally, 17 ambient air temperature, relative humidity and vegetation density constitute the main effective drivers of SC 18 atmospheric concentrations. 19

1. Introduction20

Primary biogenic organic aerosols (PBOA), which notably comprise bacterial and fungal cells or spores; viruses; 21

or microbial fragments such as endotoxins and mycotoxins; and pollens and plant debris, are ubiquitous particles 22

released from the biosphere to the atmosphere (Amato et al., 2017; Després et al., 2012; Elbert et al., 2007; Fang 23

et al., 2018; Fröhlich-Nowoisky et al., 2016; Morris et al., 2011; Wéry et al., 2017). PBOA can contribute 24

significantly to the total coarse aerosol mass (Amato et al., 2017; Bozzetti et al., 2016; Coz et al., 2010; Fröhlich-25

Nowoisky et al., 2016; Jaenicke, 2005; Manninen et al., 2014; Morris et al., 2011; Samaké et al., 2019; Vlachou 26

et al., 2018; Yue et al., 2017). Besides their expected negative human health effects (Fröhlich-Nowoisky et al., 27

2009, 2016; Humbal et al., 2018; Lecours et al., 2017), they substantially influence the carbon and water cycles at 28

the global scale, notably acting as cloud and ice nuclei (Ariya et al., 2009; Elbert et al., 2007; Fröhlich-Nowoisky 29

et al., 2016; Hill et al., 2017; Humbal et al., 2018; Morris et al., 2014; Rajput et al., 2018). While recent studies 30

have revealed highly relevant information on the abundance and size partitioning of PBOA, their emission sources 31

and contribution to total airborne particles are still poorly documented, partly due to the analytical limitations to 32

distinguish PBOA from other types of carbonaceous particulate matter (Bozzetti et al., 2016; China et al., 2018; 33

Di Filippo et al., 2013; Heald and Spracklen, 2009; Jia et al., 2010). Notably, the global emissions of fungal spore 34

emitted into the atmosphere are still poorly constrained and range from 8 Tg.y-1 to 186 Tg.y-1 (Després et al., 2012; 35

Elbert et al., 2007; Jacobson and Streets, 2009; Sesartic and Dallafior, 2011). 36

Recently, source-specific tracer methodologies have been introduced to estimate their contribution to aerosol 37

loadings (Bauer et al., 2008a; Di Filippo et al., 2013; Gosselin et al., 2016; Zhang et al., 2010, 2015). Indeed, 38

atmospheric organic aerosols (OA) contain specific chemical species that can be used as reliable biomarkers in 39

tracing the sources and abundance of PBOA (Bauer et al., 2008a; Gosselin et al., 2016; Holden et al., 2011; Jia 40

and Fraser, 2011; Medeiros et al., 2006b). For instance, sugar alcohols (aka polyols)—including arabitol and 41

mannitol (two common storage soluble carbohydrates in fungi)—have been recognized as tracers for airborne 42

fungi, and their concentrations are widely used to estimate PBOA contributions to OA mass (Amato et al., 2017; 43

Bauer et al., 2008a, 2008b; Golly et al., 2018; Medeiros et al., 2006b; Samaké et al., 2019; Verma et al., 2018; 44

Weber et al., 2018; Zhang et al., 2010; Zhu et al., 2015, 2016). Similarly, glucose has also been used as a specific 45


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tracer for plant materials (such as pollen, leaves, and their fragments) or soil emissions within various studies 46

around the world (Chen et al., 2013; Fu et al., 2013; Liang et al., 2016; Medeiros et al., 2006b; Pietrogrande et al., 47

2014; Rathnayake et al., 2017; Rogge et al., 2007; Simoneit et al., 2004b; Wan and Yu, 2007; Wan et al., 2019). 48

In this context, atmospheric concentrations of specific polyols and/or primary monosaccharides (including 49

glucose) have been previously quantified at sites in several continental, agricultural, coastal or polar regions 50

(Barbaro et al., 2015; Chen et al., 2013; Fu et al., 2012; Golly et al., 2018; Graham et al., 2003; Jia et al., 2010; 51

Liang et al., 2016; Pietrogrande et al., 2014; Rogge et al., 2007; Simoneit et al., 2004a; Verma et al., 2018; Yttri 52

et al., 2007; Zhu et al., 2018). However, large datasets investigating their (multi)annual cycles, seasonal and 53

simultaneous short-term variations at multiple spatial scale resolutions (i.e. from local to continental) are still 54

lacking (Liang et al., 2013; Nirmalkar et al., 2018; Pietrogrande et al., 2014; Yan et al., 2019). Such records are 55

essential to better understand the spatial behavior of primary sugar compound (SC) concentrations (i.e., glucose, 56

arabitol and mannitol) and PBOA emission processes, and to isolate their potential key drivers (e.g., vegetation 57

type and density, topography, weather conditions, etc.), which are still unclear (Bozzetti et al., 2016). This 58

information would be essential for further implementation into chemical transport models (Heald and Spracklen, 59

2009; Tanarhte et al., 2019). 60

It is commonly acknowledged that SC (particularly arabitol and mannitol) originate from primary biogenic derived 61

sources such as bacterial, fungal spores, and plant materials (Di Filippo et al., 2013; Golly et al., 2018; Gosselin 62

et al., 2016; Graham et al., 2003; Holden et al., 2011; Medeiros et al., 2006b; Simoneit et al., 2004b; Wan et al., 63

2019; Yan et al., 2019; Yttri et al., 2007, 2011a; Zhu et al., 2015). Some studies have characterized the composition 64

of SC in topsoil samples (for fractions larger than PM10) from both, natural (i.e., uncultivated) and agricultural 65

regions (Medeiros et al., 2006a; Rogge et al., 2007; Simoneit et al., 2004b; Wan and Yu, 2007). The authors 66

suggested that the particulate arabitol, mannitol and glucose are introduced into the atmosphere mainly through 67

resuspended soils or dust particles and associated biota derived from natural soil erosion, unpaved road dust or 68

agricultural practices. Conversely, Jia and Fraser (2011) reported higher concentrations of SC relative to PBOA in 69

size-segregated aerosol samples collected at a suburban site (Higley, USA) compared to the local size-fractionated 70

soils (equivalent to atmospheric PM2.5 and PM10). This suggested that direct emissions from biota (microbiota, 71

vascular plant materials) could also be a significant atmospheric input process for SC at this suburban site. 72

A large database on SC concentrations was obtained over France in the last decade. It already allowed the 73

investigation of the size distribution and seasonal variabilities of SC concentrations in aerosols at 28 French sites, 74

notably showing that SC are ubiquitous primary aerosols, accounting for a significant proportion of PM10 organic 75

matter (OM) mass (Samaké et al., 2019). Results confirmed that their ambient concentrations display a well-76

marked seasonality, with maximum concentrations from late spring to early autumn, followed by an abrupt 77

decrease in late autumn, and a minimum concentration during wintertime in France. This study also showed that 78

the mean PBOA chemical profile is largely dominated by organic compounds, with only a minor contribution of 79

dust particle fraction. The latter result indicated that ambient polyols could most likely be associated with direct 80

biological particle emissions (e.g. active spore discharge, microbiota released from phylloplane or phyllosphere, 81

etc.) rather than with the microorganism-containing soil resuspension. These observations call for more 82

investigations of the predominant SC (and PBOA) emission sources. 83

Cellulose, a linear polymer composed of D-glucopyranose units linked by β-1,4 bonds, is the most frequent 84

polysaccharide occurring in terrestrial environments (Ramoni and Seiboth, 2016). Plant materials contain cellulose 85


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which has been reported as a suitable proxy to evaluate the vegetative debris contribution to OM mass (Bozzetti 86

et al., 2016; Glasius et al., 2018; Puxbaum and Tenze-Kunit, 2003; Sánchez-Ochoa et al., 2007; Yttri et al., 2011b). 87

The ambient PM10 cellulose has been shown to be abundant in the European semi-rural or background 88

environments (accounting for 2 to 10 % of OM mass) (Glasius et al., 2018; Sánchez-Ochoa et al., 2007) and Nordic 89

rural environments in Norway (contributing to 12 to 18 % of total carbon mass) (Yttri et al., 2011b). Thus, 90

simultaneous concentration measurements of cellulose and SC can provide essential information into their 91

emission source dynamics. 92

As the continuation of our previous work (Samaké et al., 2019), the present paper aims to delineate the processes 93

that drive the atmospheric concentrations of SC and then PBOA. This is achieved through (i) the analysis of 94

simultaneous annual short-term time series of particulate SC concentrations over pairs of sites across multiple 95

space ranges, including local, regional and nationwide sites, and (ii) the investigation of links between 96

concentrations and series key parameters such as meteorological and phenological ones. Simultaneous annual 97

short-term concentration measurements of SC and cellulose was performed to better understand of their sources 98

correlations. 99

2. Material and methods100

2.1 Sampling sites 101

Daily PM10 concentrations reported in the present work were obtained from different research and monitoring 102

programs conducted over the last six years in France. Within the framework of the present study, we carefully 103

selected sites sharing at least one complete year of concurrent monitoring with another one, to be representative 104

of the annual variation cycles. The final dataset includes data from 16 sites, which are distributed in different 105

regions of France (Figure 1) and cover several main types of environmental conditions in terms of site topography, 106

local vegetation, and climate. The characteristics and data available at each sampling site are listed in Table S1 of 107

the supplementary material (SM), together with the information on the annual average concentrations of aerosol 108

chemical composition (Table S2). Detailed information on the sampling conditions can be found in Samaké et al. 109

(2019), such as the campaign periods, number of collected PM samples, sampling flow rates, sample storage and 110

handling, etc. Note that, the previous database (Samaké et al., 2019) has been updated here with arabitol and 111

mannitol in PM10 collected at the suburban site of Nogent-sur-Oise for a series covering the years 2013 to 2017. 112

113


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Figure 1: Geographical location of the selected sampling sites. The red and blue dots indicate respectively urban and 114 suburban sites while the green one corresponds to a rural site, surrounded by field crop areas. 115

2.2 Chemical analyses 116

Daily (24 h) PM10 samples were collected onto prebaked quartz fiber filter (Tissuquartz PALL QAT-UP 2500 150 117

mm diameter) every third or sixth day, but not concurrently at all sites. They were then analyzed for various 118

chemical species using subsampled fractions of the collection filters and a large array of analytical methods. Details 119

of all the chemical analysis procedures are reported elsewhere (Golly et al., 2018; Samaké et al., 2019; Waked et 120

al., 2014; Weber et al., 2018). Briefly, primary sugar compounds were extracted from filter aliquots (punches 121

typically about 10 cm²) into ultrapure water. The extracts are then filtered using a 0.22 µm Acrodisc filter. 122

Depending on the site, analyses were conducted either by the IGE (Institut des Géosciences de l’Environnement) 123

or by the LSCE (Laboratoire des Sciences du Climat et de l’Environnement) (Samaké et al., 2019). At the IGE, 124

extraction was performed during 20 min in a vortex shaker and analyses were achieved using high-performance 125

liquid chromatography with pulsed amperometric detection (HPLC-PAD). A first set of equipment was used until 126

March 2016, consisting of a Dionex DX500 equipped with three columns Metrosep (Carb 1-Guard + A Supp 15-127

150 + Carb 1-150), the analytical program was isocratic with 70 mM sodium hydroxide (NaOH) as eluent for 11 128

min, followed by a gradient cleaning step with a 120 mM NaOH as eluent for 9 min. This procedure allows the 129

analysis of arabitol, mannitol and glucose (Waked et al., 2014). A second set of equipment was used after March 130

2016, with a Thermo-Fisher ICS 5000+ HPLC equipped with 4 mm diameter Metrosep Carb 2 × 150 mm column 131

and 50 mm pre-column. The analytical run was isocratic with 15 % of an eluent of sodium hydroxide (200 mM) 132

and sodium acetate (4 mM) and 85 % water, at 1 mL min-1. At the LSCE, extraction was performed for 45 min by 133

sonication and analyses were achieved using ion chromatography instrument (IC, DX600, Dionex) with Pulsed 134

Amperometric Detection (ICS3000, Thermo- Fisher). In addition, a CarboPAC MA1 column has been used (4 × 135

250 mm, Dionex) along with an isocratic analytical run with 480 mM sodium hydroxide eluent. This analytical 136

technique allows to quantify arabitol, mannitol and glucose (Srivastava et al., 2018). 137

For cellulose quantification, we used an optimized protocol based on that described by (Kunit and Puxbaum, 1996; 138

Puxbaum and Tenze-Kunit, 2003), in which the cellulose contained in the lignocellulosic material is enzymatically 139

hydrolyzed into glucose units before analysis. Since the alkaline peroxide pretreatment step used to remove lignin 140

in the original protocol results in a loss of sample material, it has been avoided in this study. Therefore, only the 141

“free cellulose” is reported in our samples. Note that Sánchez-Ochoa et al., (2007) consider that this free cellulose 142

could represent only about 70 % of the total cellulose in air samples and that the total cellulose could represent 143

only about 50 % of the “plant debris” content of atmospheric PM. Very few other results are available on this topic 144

(Bozzetti et al., 2016; Glasius et al., 2018; Vlachou et al., 2018; Yttri et al., 2011b). The protocol has been improved 145

to increase sensitivity and accuracy, by reducing the contribution of glucose in the blanks and by using an HPLC-146

PAD as the analytical method for the determination of glucose concentrations. Trichoderma reesei cellulase (>700 147

u g-1, Sigma Aldrich) and Aspergilus Niger glucosidase (>750 u g-1, Sigma Aldrich) have been used as148

saccharification enzymes. The protocol is detailed in Section 2 of the SM. 149

Field blank filters (about 10 % of samples) were handled as real samples for quality assurance. The present data 150

have been corrected from field blanks. The reproducibility of the analysis of primary sugar compounds (polyols, 151

glucose) and cellulose, estimated from the analysis of sample extracts from 10 punches of the same filters were in 152


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the range of 10-15 %. About 2 800 samples are considered in this work for the polyols and glucose series, while 153

290 samples (from the sites of Grenoble_LF and OPE-ANDRA) are considered for the cellulose series. 154

2.3 Meteorological data and LAI measurements155

Ambient weather data were not available at all monitoring sites (see Table S1). In this study, data including daily 156

relative humidity (%), night-time temperature (°C), average and maximum temperatures (°C), wind speed (m s-1), 157

solar radiation (W m-2), and rainfall level (mm) for the sites of Marnaz and OPE-ANDRA (Figure 1), representing 158

different climatic regions and environmental conditions, were obtained from the French meteorological data 159

sharing service system (Météo-France) and ANDRA (French national radioprotective agency, in charge of the 160

OPE-ANDRA site), respectively. 161

The leaf area index (LAI), which is defined as the projected area of leaves over a unit of land, is an important 162

measure of the local vegetation density variation (Heald and Spracklen, 2009; Yan et al., 2016a, 2016b). For this 163

study, we used the MODIS Collection 6 LAI product because it is considered to have the highest quality among 164

all the MODIS LAI products (Yan et al., 2016a, 2016b). The MCD15A3H product uses both Terra and Aqua 165

reflectance observations as inputs to estimate daily LAI at 500 m spatial resolution, and a 4-day composite is 166

calculated to reduce the noise from abiotic factors. Using a 2 × 2 km grid box around the monitoring site, the local 167

vegetation density variation was retrieved from LP DAAC (https://lpdaac.usgs.gov/, last accessed: 15 March 2019) 168

for the sites of Marnaz, OPE-ANDRA, and Grenoble_LF. 169

2.4 Data analyses 170

All the statistical analyses were carried out using the open-source R software (R studio interface, version 3.4.1). 171

Several statistical analyses were performed on the concentrations to identify the spatial patterns of emission 172

sources and the potential parameters of influence as explained below. 173

The normalized cross-correlation (NCC) test was chosen to examine the potential similarities among the 174

monitoring sites for particulate SC concentrations, in terms of short-term temporal trends (e.g. synchronized 175

periods of increase or decrease, simultaneous fluctuations during specific episodes). The main advantage of NCC 176

over the traditional correlation tests is that it is less sensitive to linear changes in the amplitudes of the two-time 177

series compared. Therefore, to reduce the possibility of spurious “anti-correlation” due to highly variable 178

concentration ranges, data were amplitude-normalized prior to correlation analysis. A thorough discussion on the 179

normalized cross-correlation method can be found elsewhere (Kaso, 2018; Yoo and Han, 2009). To achieve pair-180

wise correlation analysis between the sampling sites collected during the same periods, the original daily 181

measurements were processed as follows: starting on identical days, arrangement on the original daily data into 182

consecutive 3-day intervals (or 6-day intervals in the case of OPE-ANDRA) and calculation of the average 183

concentration values for the middle-day were performed. The resultant data were used for correlation analysis 184

(Table S3). 185

Multiple linear regression (MLR) was used to assess the strength of the relationships between atmospheric 186

concentrations of particulate SC and local environmental factors including the daily mean relative humidity, night-187

time temperature, average and maximum temperature, wind speed, solar radiation, rain levels and LAI. Because 188

the LAI is a 4-day composite, daily values of the other variables were re-scaled into consecutive 4-day averaged 189

values. The linear regression (lm) package in R was employed for multiple regression analyses. The concentration 190

data were log-transformed to obtain regression residual distributions as close as possible to the normal Gaussian 191


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one (Figure S1). Stepwise forward selection was used to select the predictors that explain well the temporal 192

variation of SC concentrations at the site of Marnaz. 193

It should be noted that due to the limited availability of external parameters, the environmental factors driving SC 194

atmospheric levels have been extensively investigated for only two monitoring sites with contrasted 195

characteristics: the urban background site of Marnaz located in an Alpine valley, and the rural OPE-ANDRA site 196

surrounded by field crop areas spreading over several tens of km. 197

198

3. Results and discussion199 3.1 Example of spatial coherence of the concentrations at different scales200

Our previous work (Samaké et al., 2019) showed that particulate polyols and glucose are ubiquitous primary 201

compounds with non-random spatial and seasonal variation patterns over France. Here, an inter-site comparison 202

of their short-term concentration evolutions has been carried out at different space scales (from local to national) 203

for the pairs that can be investigated in our data base. Figure 2 presents some of these comparisons for 3 spatial 204

scales (15, 120, and 205 km). 205

The daily average concentrations of polyols (defined as sum of arabitol and mannitol) and glucose display highly 206

synchronous evolutional trends (i.e., homogeneity in the concentrations, the timing of concentration peaks, 207

simultaneity of the daily specific episodes of increase/decrease of concentrations) over 3 neighboring monitoring 208

sites located 15 km apart in the Grenoble area (Figures 2A and B). Interestingly, remarkable synchronous patterns 209

both for short term (near-daily) and longer term (seasonal) still occur for sites located 120 km apart, as exemplified 210

for 2 sites in Alpine environments (Grenoble and Marnaz) (Figures 2C and D). However, as shown in Figures 2E 211

and F, the evolutions of concentrations become quite dissimilar and asynchronous in terms of seasonal and daily 212

fluctuations for more distant sites (Grenoble and Nice, 205 km apart), that are located in different climatic regions 213

(Alpine for Grenoble, Mediterranean for Nice). This is contrasting with results from the rural background site of 214

OPE-ANDRA and the suburban site of Nogent-sur-Oise, both located in a large field crop region of extensive 215

agriculture, and about 230 km apart from each other (Figure 2G). Indeed, they present very similar variations of 216

daily concentrations for multi-year series, despite their distance apart, with concentration peaks generally more 217

pronounced at the rural site of OPE-ANDRA. 218

The following sections are dedicated to the investigation of the processes that can lead to these similarities and 219

differences according to these spatial scales. 220

221


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Figure 2: Concentrations (in ng m-3) of (left) ambient particulate polyols (defined as the sum of arabitol and mannitol) 223 and glucose (right) over different monitoring sites in France. Since PM10 were collected every 3-days at Nogent-sur-Oise 224 and 6-days at OPE-ANDRA, the original data sets are averaged over consecutive 6-day intervals (bottom graph). 225

3.2 Inter-site correlations and spatial scale variability 226

Figures 3A and 3B provide an overview of the cross-correlation coefficients for the daily evolution of 227

concentrations (for glucose and polyols (SC)) between pairs of sites located at multiple increasing space scales 228

across France (Table S3). Time series of concentrations for both SC show a clear distance-dependent correlation. 229

The strength of the correlations is highly significant for distances up to 150-190 km (R > 0.72, p < 0.01) and 230

gradually decreases with increasing inter-site distances. One exception is the pair OPE-ANDRA and Nogent-sur-231

Oise (high correlation for a distance above 230 km), both sites being located in highly-impacted agricultural areas. 232

This overall pattern suggests that the processes responsible for the atmospheric concentrations of SC present a 233

spatial homogeneity over typical areas of at least several tens of km 234


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235

Figure 3: Normalized cross-correlation values for the daily evolution of particulate glucose (A) and polyols (B) 236 concentrations over pairs of sites located at multiple increasing space scales across France. The hexagram corresponds 237 to the correlation between the sites of OPE-ANDRA and Nogent-sur-Oise, both sites being surrounded by crop field 238 areas. 239

Unlike SC, ambient air concentrations of sulfate, associated with long-range aerosol transport (Abdalmogith and 240

Harrison, 2005; Amato et al., 2016; Coulibaly et al., 2015; Pindado and Perez, 2011; Waked et al., 2014) display 241

stronger positive correlations (R > 0.72-0.98, p < 0.01) at all pairs of sites considered in the present work (Figure 242

S2). Moreover, ambient concentrations of calcium, associated with local fugitive dust sources or/and long-range 243

aerosol transport (Ram et al., 2010; Wan et al., 2019) display random correlation patterns (Figure S2). These results 244

are in agreement with Zhu et al. (2018) who also reported non-significant correlations between SC and sulfate in 245

PM2.5 aerosols measured at Shanghai, China. The distinct spatial behaviors between sulfate (or Ca2+) and SC in the 246

present work further suggest a dominant regional influence for atmospheric SC, as opposed to processes associated 247

with either local sources for calcium or long-range transport for sulfate. 248

Mannitol and arabitol are well-known materials of fungal spores, serving as osmo-regulatory solutes (Medeiros et 249

al., 2006b; Simoneit et al., 2004b; Verma et al., 2018; Zhang et al., 2010, 2015). Based on parallel measurements 250

of spore counts and PM10 polyol concentrations at three sites within the area of Vienna (Austria), Bauer et al. 251

(2008a) found an average arabitol and mannitol content per fungal spores of respectively 1.2 pg spore-1 (range 0.8-252

1.8 pg spore-1) and 1.7 pg spore-1 (range 1.2-2.4 pg spore-1). Mannitol and arabitol have also been often identified 253

in the green algae and lower plants (Buiarelli et al., 2013; Di Filippo et al., 2013; Vélëz et al., 2007; Xu et al., 254

2018; Zhang et al., 2010). Being important chemical species for the metabolism of these microorganisms 255

(Shcherbakova, 2007), it may well be that the concentration ratio of mannitol-to-arabitol could deliver some 256

information on the spatial or temporal evolution of their emission processes (Gosselin et al., 2016). The annual 257

average mannitol-to-arabitol ratio at all sites is about 1.15 ± 0.59, with ratios for the warm period (Jun-Sept) being 258

1 to 2 times higher than those in the cold period (Dec-May) (Table S1). These ratios are within the range of those 259

previously reported for PM10 aerosols collected at various urban and rural background sites in Europe (Bauer et 260

(B) (A)


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al., 2008a; Yttri et al., 2011b). Similarly, Burshtein et al., (2011) also reported comparable ratios for PM10 aerosols 261

collected during autumn and winter from a Mediterranean region in Israel. 262

Similarly, the annual average glucose-to-polyols ratio at all sites is about 0.79 ± 0.77. No literature data are 263

currently available for comparison. Further work is needed to relate these variations with microorganism 264

communities and plant growing stages. 265

However, as evidenced in Figure 4, both mannitol-to-arabitol and glucose-to-polyols ratios show a clear distance-266

dependent correlation, with higher correlations (R = 0.64 to 0.98, p < 0.01) observed for pairs of sites within 150-267

190 km distance. This spatial consistency highlights once again that the dominant emission processes should be 268

effective regionally, rather than being specific local input processes, and that atmospheric dynamics of the 269

concentration levels (i.e., driven by the interplay of emission and removal processes) are determined by quite 270

similar environmental factors (e.g. meteorological conditions, vegetation, land use, etc.) at such a regional scale. 271

This implies that local events and phenomena, such as the mechanical resuspension of topsoil and associated biota 272

(like bacteria, fungi, plant materials, etc.) might not be their major atmospheric input processes, particularly in 273

urban background areas typically characterized by less bare soil, and with a variable nature of the unpaved topsoil 274

at the regional scale (Karimi et al., 2018). Furthermore, Karimi et al. (2018) also recently reported heterogeneous 275

topsoil microbial structure within patches of 43 to 260 km across different regions of France. It follows that the 276

hypotheses of emissions related to mechanical resuspension of topsoil particles and associated biota, or microbiota 277

emitted actively from surface soil into the air generally assumed in most pioneering reports (Medeiros et al., 2006b; 278

Rogge et al., 2007; Simoneit et al., 2004b; Wan and Yu, 2007) are most probably not valid. 279

Alternatively, the vegetation leaves have also been suggested as sources of atmospheric SC (Golly et al., 2018; Jia 280

and Fraser, 2011; Pashynska et al., 2002; Sullivan et al., 2011; Verma et al., 2018; Wan et al., 2019). In fact, 281

vascular plant leaf surfaces is an important habitat for endophytic and epiphytic microbial communities (Kembel 282

and Mueller, 2014; Lindow and Brandl, 2003; Whipps et al., 2008). Our results are more in agreement with a 283

dominant atmosphere entrance process closely linked to vegetation, which is more homogeneous than topsoil at 284

the climatic regional scale. Consistent with this, Sullivan et al. (2011) also observed evident distinct regional 285

patterns for daily PM2.5 polyols and glucose concentrations at ten urban and rural sites located in the upper Midwest 286

(USA). The authors attributed such a spatial pattern to the differences in vegetation types and microbial diversity 287

over distinct geographical regions. Accordingly, the vegetation structure and composition have been previously 288

shown to play essential roles on airborne microbial variabilities in nearby areas (Bowers et al., 2011; 289

Lymperopoulou et al., 2016; Mhuireach et al., 2016). 290


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291

Figure 4: Normalized cross-correlation values for daily evolution of particulate glucose-to-polyols (A) and mannitol-to-292 arabitol (B) ratios over pairs of sites located at multiple increasing space scales across France. The hexagram 293 corresponds to the correlation between the sites of OPE-ANDRA and Nogent-sur-Oise, both sites being surrounded by 294 crop field areas. 295

3.3 Influence of the vegetation on polyols and glucose concentrations 296

The relationships between SC PM10 concentrations and vegetation (plant materials) can be examined at the site of 297

Grenoble Les Frênes (Grenoble_LF) by comparing the annual evolutions of SC and the free atmospheric cellulose 298

concentrations, together with LAI ones. 299

The daily ambient concentration levels of SC and cellulose range respectively from 5.0 to 301.9 ng m-3 (with an 300

average of 41.2 ± 39.9 ng m-3) and 0.7 to 207.2 ng m-3 (with an average of 52.9 ± 44.2 ng m-3), which corresponds 301

to respectively to 0.1 to 6.6 % and 0.01 to 5.3 % of total organic matter (OM) mass in PM10. These values are 302

comparable to those previously reported for various sites in Europe (Daellenbach et al., 2017; Sánchez-Ochoa et 303

al., 2007; Vlachou et al., 2018; Yttri et al., 2011b). Thus, a major part of PBOA could possibly be ascribed cellulose 304

and SC derived sources. 305

As evidenced in Figure 5A, ambient free cellulose concentrations vary seasonally, with maximum seasonal average 306

values observed in summer (81.4 ± 47.6 ng m-3) and autumn (64.2 ± 49.2 ng m-3), followed by spring 307

(52.6 ± 37.8 ng m-3), and lower levels in winter (23.0 ± 19.9 ng m-3). This is the same global pattern for polyols, 308

that are also more abundant in summer (82.4 ± 47.4 ng m-3) and autumn (48.7 ± 41.6 ng m-3), followed by spring 309

(24.9 ± 16.3 ng m-3), and winter (10.2 ± 9.6 ng m-3) in the Grenoble area. On a daily scale, the episodic increases 310

or decreases of polyols in PM10 are very often well synchronized with that of cellulose (figure 5A). Moreover, the 311

maximum atmospheric concentrations of polyols also mainly occur when the vegetation density (LAI) is at its 312

highest in late summer (Figure 5B). Similar global behaviors are also observed for atmospheric particulate glucose 313

and LAI (Figs. 5A and B). To further assess the relationships between SC PM10 concentrations and vegetation at 314

a rural area, a two-year measurement of cellulose concentrations at the highly-impacted agricultural rural site of 315

OPE-ANDRA has been conducted. The average concentration of cellulose at OPE-ANDRA (197.9 ± 217.8 ng m-316

(A) (B)


12

3) is 3.5 times higher than that measured in the urban area of Grenoble. In terms of temporal dynamics, the 317

evolution cycles (i.e., peaks and decreases) of both polyols and glucose are also very often well synchronized with 318

that of cellulose at OPE-ANDRA (Fig. 5C). 319

Altogether, these findings highlight that particulate SC PM10 and cellulose in both urban background and rural 320

agricultural areas most probably share a common source related to the vegetation. This is an additional evidence 321

in support of the hypothesis suggested in previous studies (Bozzetti et al., 2016; Burshtein et al., 2011; Daellenbach 322

et al., 2017; Pashynska et al., 2002; Verma et al., 2018; Vlachou et al., 2018; Yttri et al., 2007). It is also in line 323

with studies indicating that the PBOA source profile identified using offline aerosol mass spectrometry (offline-324

AMS) correlates very well with coarse cellulose concentrations (Bozzetti et al., 2016; Vlachou et al., 2018). 325

Noticeable contribution of cellulose to PBOA mass (26 %) at the rural background site of Payerne (Switzerland), 326

during summer 2012 and winter 2013, was reported by (Bozzetti et al., 2016). 327

As also evidenced in Figure 5, the cellulose concentration peaks are not systematically correlated to those of 328

polyols. The development stage of the plants (developing or mature leaves, flowering plants) in addition to the 329

metabolic activities of endophytic and epiphytic biota (growth, sporulation), all closely related to meteorological 330

conditions (Bodenhausen et al., 2014; Bringel and Couée, 2015; Lindow and Brandl, 2003; Moricca and Ragazzi, 331

2011; Reddy et al., 2017), could explain such observations. The influence of local meteorological conditions for 332

an urban Alp valley site is discussed in Section 3.4. Consistent with our observations, previous studies conducted 333

at various urban background sites in Europe have suggested that particulate polyols are associated to mature plant 334

leaves and microorganisms (bacterial and fungal spores) while glucose, which is a monomer of cellulose, would 335

most likely be linked to the developing leaves (Bozzetti et al., 2016; Burshtein et al., 2011; Pashynska et al., 2002; 336

Yttri et al., 2007). 337


13

338

Figure 5: Temporal covariation cycles of the daily particulate polyols and glucose concentrations along with vegetation 339 indicators at the urban background site of Grenoble (A and B) and the rural agricultural background site of OPE-340 ANDRA (C), respectively. Note that PM10 aerosols are intensively collected at OPE-ANDRA every day (24-h) from 12 341 June 2017 to 22 August 2017, and that the concentration scale is changing above 600 ng m-3 in Figure C, due to extreme 342 concentration peak in July 2017. 343

3.4 Influence of meteorological parameters on ambient concentrations of polyols and glucose 344

We used here a multiple linear regression analysis (MLR) approach to gain further insight about the environmental 345

factors influencing the annual and short time variation cycles of atmospheric SC concentrations. This tentative 346

MLR analysis is focused on the urban background site of Marnaz only since meteorological and other data are 347

readily available for this site and are not influenced too much by some large city effects. Several variables were 348

tested, that are already mentioned in the literature as drivers of SC concentrations. It includes the ambient relative 349

humidity, rainfall level, wind speed, solar radiation, night-time temperature, average (or maximum) temperature, 350

and LAI. Night-time temperature was selected since the time series in Marnaz and Grenoble indicate that the major 351

drop of concentrations in late fall (Figure 2C) is related to the first night of the season with night-time temperature 352

below 5°C. The use of the night-temperature is also consistent with the bi-modal distribution of polyols during 353

night and day time found in previous studies (Claeys et al., 2004; Graham et al., 2003). 354

Overall, the environmental factors including the mean night-time temperature, relative humidity, wind speed and 355

the leaf area index explain up to 82 % (adjusted R2 = 0.82, see Table 1) of the annual temporal variation cycles of 356

SC concentrations. The mean night-time temperature and LAI contribute respectively to 54 % and 37 % of the 357

observed annual variabilities of SC concentrations. The atmospheric humidity is also a driver for these chemical 358

species (3 % of the explained variation). These results are consistent with previous studies showing that 359


14

concentrations of mannitol (in both PM10 and PM2.5 size fractions) linearly correlate best with the LAI, atmospheric 360

water vapor and temperature (Heald and Spracklen, 2009; Hummel et al., 2015). All of these drivers have been 361

previously shown to induce the initial release and influence the long-term airborne microbial (i.e. bacteria, fungi) 362

concentrations (China et al., 2016; Elbert et al., 2007; Grinn-Gofroń et al., 2019; Jones and Harrison, 2004; 363

Rathnayake et al., 2017; Zhang et al., 2015). 364

Besides, the wind speed (range of 0.2 to 5.6 m s-1) seems an additional effective driver affecting the contribution 365

of the local vegetation to SC concentrations in the atmosphere. Albeit enough air movement is required to passively 366

release microorganisms along with plant debris into the atmosphere, strong air motions induce higher dispersion. 367

These observations are in good agreement with those previously reported (Jones and Harrison, 2004; Liang et al., 368

2013; Zhang et al., 2010, 2015; Zhu et al., 2018). For instance Liang et al. (2013) have found a negative correlation 369

between wind speed and polyols concentrations, and the highest atmospheric fungal spores concentrations were 370

observed for a wind speed range of 0.6 to 1.0 m s-1. 371

Table 1: Multiple linear regression for ambient polyols and glucose concentrations and their effective environmental 372 factors at the Marnaz site. Contributions of predictor are normalized to sum 1. “Relaimpo package under R” was 373 used to compute bootstrap confidence intervals for importance of effective predictors (n=1000) (Grömping, 2006). 374

Dependent variable Variability explained by effective predictors

log(Polyols + Glucose)

Night-time temperature (°C) 0.112*** (0.090, 0.133) 0.538 (0.453, 0.604) Relative Humidity (%) 0.017*** (0.005, 0.030) 0.030 (0.018, 0.067)

Leaf Area Index 0.386** (0.034, 0.737) 0.372 (0.286, 0.444) Wind speed (m s-1) 0.226 (-0.203, 0.655) 0.021 (0.015, 0.058)

Leaf Area Index × Wind Speeda -0.596*** (-1.001, -0.191) 0.039 (0.014, 0.085) Constant 2.023*** (0.787, 3.260)

Observations 87 R2 0.837

Adjusted R2 0.824

Residual Std. Error 0.297 (df = 81)

F Statistic 66.677*** (df = 5; 81)

Note **p < 0.01; ***p < 0.001 a stands for interaction between predictors . 375

One of the limitations of this study is that 4-day averaged observations do not allow to evaluate the driver 376

contributions that might explain some short term events for which the influence of meteorological parameters such 377

as rainfall or solar radiation could also be significant (Grinn-Gofroń et al., 2019; Heald and Spracklen, 2009; Jones 378

and Harrison, 2004). However, such simple parameterizations could be a first step in considering SC 379

concentrations in CTM models, and further work is required in this direction in order to generate a robust 380

parametrization of the emissions. 381

3.5 Specific case of a highly-impacted agricultural area 382

This section focuses on evidencing the environmental drivers of PM10 SC concentrations specific to agricultural 383

areas. To achieve this objective, the site of OPE-ANDRA has been selected because it is extensively impacted by 384

agricultural activities, without being too prone to influences by other sources. OPE-ANDRA is a specific rural 385

background observatory located at about 230 km east of Paris at an altitude of 293 m. It is characterized by a low 386

population density (< 22 inhabitants km-2 within an area of 900 km2), with no surrounding major transport road or 387

industrial activities. The air monitoring site itself lies in a “reference sector” of 240 km2, in the middle of a field 388

crop area (tens of kilometers in all directions). The daily agricultural practices within this reference sector are 389


15

recorded and made available by ANDRA. The parcels within the agricultural area are submitted to a 3-year crop-390

rotation system. The major crops are wheat, barley, rape, pea and sunflower. Additionally, OPE-ANDRA is also 391

characterized by a homogeneous type of soil, with a predominance of superficial clay-limestone. 392

Figure 6 shows the daily evolution of polyols concentrations in the PM10 fraction at OPE-ANDRA from 2012 to 393

2018, together with the agricultural activities recorded daily and averaged over 12 days. 394

Although the concentration of polyols fluctuates from a year to another, they display clear annual variation cycles, 395

with higher values in the warm periods (Jun. to Nov.) and lower concentration values in the cold periods (Oct. to 396

May). Interestingly, the annual concentrations of polyols in 2015 (4.2-111.7 ng m-3; annual average: 397

37.0 ± 29.1 ng m-3) are significantly lower than those observed for the other years (0.6-1084.6 ng m-3; annual 398

average: 62.9 ± 96.8 ng m-3). Similar inter-annual evolution trends, but with variable intensities, are also observed 399

for glucose concentrations (Figure 6). Year 2015 has been found to be particularly hot and dry at OPE-ANDRA 400

(Figure 7) whereas the local averaged wind conditions are quite stable over the years within the period of study, 401

suggesting that the wind conditions are not the main driver of the observed inter-annual variability. These results 402

highlight that ambient air temperature and humidity are key meteorological drivers of the annual variation cycles 403

of polyols and glucose concentrations. Hot and dry ambient air conditions may decrease the metabolic activity of 404

the microorganisms (e.g. microbial growth and sporulation) (Fang et al., 2018; Liang et al., 2013; Meisner et al., 405

2018). 406

Finally, maximum ambient concentration levels for both SC and cellulose are observed in excellent temporal 407

agreement with the harvest periods (late summer) at the ANDRA-OPE site (Figure 6). Harvesting activities have 408

been previously reported as the major sources for particulate polyols and glucose to the atmosphere in agricultural 409

and nearby urbanized areas (Golly et al., 2018; Rogge et al., 2007; Simoneit et al., 2004b). Hence, the resuspension 410

of plant materials (crop detritus, leaves debris) and associated microbiota (e.g., bacteria, fungi) originating from 411

cultivated lands are most-likely major input processes of PM10 polyols and glucose at field crop sites. 412

413

Figure 6: Daily evolution cycles of polyols and glucose concentrations in aerosols collected from the OPE-ANDRA 414 monitoring site, from 2012 to 2018. Cellulose concentrations have been measured from January 2016 to January 2018. 415 Colored bars correspond to the sum of the various agricultural practices performed (data for 69 parcels are averaged 416 over 12 days for better clarity). Records of agricultural activities after October 2014 were available for only two parcels 417 within the immediate vicinity of the PM10 sampler. Records are multiplied by 10 for this period. 418


16

419

Figure 7: Boxplots of (A) maximum ambient temperature, (B) relative humidity and (C) wind speed at OPE-ANDRA 420 from 2012 to 2017. Analyses are performed for warmer periods (June to November). Only statistically different 421 meteorological factors are presented. The black marker inside each boxplot indicates the average value, while the top, 422 middle and bottom of the box represent the 75th, median and 25th percentiles, respectively. The whiskers at the top and 423 bottom of the box extend from the 95th to the 5th percentiles. Statistical differences between average values were assessed 424 with the Kruskall-Wallis method (p < 0.05). 425

4. Conclusions426

The short-term temporal (daily) and spatial (local to nation-wide) evolutions of particulate polyols and glucose 427

concentrations are rarely discussed in the current literature. The present work aimed at investigating the spatial 428

behavior of these chemicals and evidencing their major effective environmental drivers. The major results mainly 429

showed that: 430

The short-term evolution of ambient polyols and glucose concentrations is highly synchronous across an431

urban city-scale and remains very well correlated throughout the same geographic areas of France, even432

if the monitoring sites are situated in different cities at about 150-190 km. However, sampling sites433

located in two distinct geographic areas are poorly correlated. This indicates that emission sources of434

these chemicals are uniformly distributed, and their accumulation and removal processes are driven by435

quite similar environmental parameters at the regional scale. Therefore, local phenomena such as436

atmospheric resuspension of topsoil particles and associated microbiota, microbial direct emissions (e.g.437

sporulation), cannot be the main emission processes of particulate polyols and glucose in urban areas not438

directly influenced by agricultural activities.439

The atmospheric concentrations of polyols (or glucose) and cellulose display remarkably synchronous440

temporal evolution cycles at the background urban site of Grenoble, indicating a common source related441

to plant debris.442

Higher ambient concentrations of polyols and glucose at the rural site of OPE-ANDRA occur during each443

harvest period, pointing out resuspension processes of plant materials (crop detritus, leaves debris) and444

associated microbiota for agricultural and nearby urbanized areas. This is associated with higher PM10445

cellulose concentration levels, as high as 0.4 to 2.0 µg.m-3 on a daily basis (accounting up to 7.5 to 32.4 %446

of the OM mass).447

Multiple linear regression analysis of the yearly series from the site of Marnaz gave insightful information448

on which parameter controls the ambient concentrations of polyols and glucose. Ambient air night-time449

temperature, relative humidity and vegetation density are the most important drivers, whilst wind speed450

conditions tend to affect the contribution of local vegetation.451


17

Altogether, these results improve our understanding of the spatial behavior tracers of PM10 PBOA emission sources 452

in France, and in general, which is imperative for further implementation of this important mass fraction of OM 453

into chemical transport models. Further investigations of airborne microbial fingerprint (bacteria and fungi) are 454

ongoing, which may deepen our understanding of the PBOA source profile. 455

Acknowledgements: We would like to express special acknowledgements to Pierre Taberlet (LECA, Grenoble, 456 France) for fruitful discussions about the importance of endophytic and epiphytic biota for aerobiology. The PhD 457 of AS and SW are funded by the Government of Mali and ENS Paris, respectively. We gratefully acknowledge 458 the LEFE-CHAT and EC2CO programs of the CNRS for financial supports of the CAREMBIOS multidisciplinary 459 project, and the LEFE-CHAT program for the MECEA project for the development of the atmospheric cellulose 460 measurements. Samples were collected and analyzed in the frame of many different programs funded by ADEME, 461 Primequal, the French Ministry of Environment, the CARA program led by the French Reference Laboratory for 462 Air Quality Monitoring (LCSQA), ANDRA, and actions funded by many AASQA, IMT Lille Douai (especially 463 Labex CaPPA ANR-11-LABX-0005-01 and CPER CLIMIBIO projects). Analytical aspects were supported at 464 IGE by the Air-O-Sol platform within Labex OSUG@2020 (ANR10 LABX56). We acknowledge the work of 465 many engineers in the lab at IGE for the analyses (Aude Wack, Céline Charlet, Fany Donaz, Fany Masson, Sylvie 466 Ngo, Vincent Lucaire, Claire Vérin, and Anthony Vella). Finally, the authors would like to kindly thank the 467 dedicated efforts of many other people at the sampling sites and in the laboratories for collecting and analyzing 468 the samples. 469

Author contributions: JLJ was the (co-)supervisor for the PhD for AS, FC, SW, and for the post-doc of DS, 470 BG, and AW. He directed all the personnel who performed the analysis at IGE. He is the coordinator for the CNRS 471 LEFE-EC2CO CAREMBIOS program that is funding the work of AS. GU and JMF-M were the co-supervisor for 472 the PhD of AS or SW. EP, OF, and VR supervised the PhD of DMO who investigated the sites in northern France. 473 OF, JL-J, JL-B, AA and NM were coordinating and partners of the different initial programs for the collection and 474 chemical analysis of the samples. VJ developed the analytical techniques for polyols and cellulose measurements. 475 TC performed the cellulose measurements. Samples analyses at LSCE were performed by NB. AC gave advices 476 for the statistical aspects of the data processing. AS and JLJ processed the data and wrote up the manuscript. SW 477 participated to the visualization of the results. SC is supervising the OPE station and collected the agricultural 478 activities records. All authors from AASQA (author affiliation nos. 7 to 14) are representatives for each network 479 that conducted the sample collection and the general supervision of the sampling sites. All authors reviewed and 480 commented on the manuscript. 481

Competing interests: The authors declare that they have no conflict of interest. 482

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